Interferometry constitutes a technique of acquisition height information with a range of applications, such as Digital Surface Model (DSM) generation in order to monitoring the Earth's surface. This work is focused on interferometric DSM creation utilizing radar data of Sentinel-1 and TerraSAR-X missions, covering the wider area of Northern Peloponnese. This area is characterized of loose geological formations and intense active tectonics resulting in continuous and intense relief changes. In this context, the accuracy and the update of the DSMs is essential in order to detect and map any terrain change. The selection of Sentinel-1 and TerraSAR-X images was based on the fact that both missions provide timely, with short revisiting period and satisfactory spatial resolution data. In particular, two ranges of radar data from both missions were submitted in interferometric process aimed at DSM creation. The produced DSMs were compared both visually and statistically to a very accurate reference DSM produced from airphotos by the Greek Cadastral. Furthermore, in order to estimate the accuracy of the DSMs and detect variations of terrain’s surface, points of known elevation have been used. 2D RMSE, correlation and the percentile value were computed and the results are presented.
One of the main difficulties encountered in Differential Interferometry (DInSAR) applications is temporal and spatial decorrelation over time. Single pixels, called Permanent Scatterers (PS), overcome this difficulty since they are coherent over time and over wide look-angle variations. Permanent Scatterers identification using interferographic techniques is unfeasible since they require the use of many acquisitions. Samsonov and Tiampo have presented a technique that selects Permanent Scatterers by analyzing their Polarization Phase Difference (PPD). The PPD approach would work just fine looking for single bounce scatterers because they are invariant to any initial arbitrary rotation between the scatterer and the radar Line of Sight (LOS). We propose to replace the PPD technique with Cameron’s Coherent Target Decomposition (CTD) because it is more accurate in finding the single and double bounce scatterers as it eliminates the initial orientation angle of the scatterer. Additionally, Cameron’s CTD is capable of recognizing more scattering mechanisms which means that more pixels, depending on their amplitude and stability over time, can be classified as Permanent Scatterers. A sample scene of fully polarimetric SAR image depicting the San Francisco bay was employed for experimentation. Our results demonstrate the superiority of the Cameron's CTD approach compared to PPD’s approach for the selection of pixels classified as Permanent Scatterers.
A high-resolution image is reconstructed from a sequence of subpixel shifted, aliased low-resolution frames, by means of stochastic regularized super-resolution (SR) image reconstruction. The Tukey (T), Lorentzian (L), and Huber (H) cost functions are employed for the data-fidelity term. The performance of the particular error norms, in SR image reconstruction, is presented. Actually, their employment in SR reconstruction is preceded by dilating and scaling their influence functions to make them as similar as possible. Thus, the direct comparison of these norms in rejecting outliers takes place. The bilateral total variation (BTV) regularization is incorporated as a priori knowledge about the solution. The outliers effect is significantly reduced, and the high-frequency edge structures of the reconstructed image are preserved. The proposed TTV, LTV, and HTV methods are directly compared with a former SR method that employs the L1-norm in the data-fidelity term for synthesized and real sequences of frames. In the simulated experiments, noiseless frames as well as frames corrupted by salt-and-pepper noise are employed. Experimental results verify the robust statistics theory. Thus, the Tukey method performs best, while the L1-norm technique performs inferiorly to the proposed techniques.
The use of satellite imagery and specifically SAR data for vessel detection and identification has attracted
researchers during the last decade. The objective of this work is to provide a novel approach for ship identification based
mainly on polarimetric data, taking into consideration the different behaviour of the ship in various polarizations. For
this purpose new measures and accordingly a new feature vector is proposed. The feature vector is employed in order to
create a vessel signatures database and its efficiency is tested on ASAR data.
The objective evaluation of the performance of pixel level fusion methods is addressed. For this purpose a global measure based on information theory is proposed. The measure employs mutual and conditional mutual information to assess and represent the amount of information transferred from the source images to the final fused gray-scale image. Accordingly, the common information contained in the source images is considered only once in the performance evaluation procedure. The experimental results clarify the applicability of the proposed measure in comparing different fusion methods or in optimizing the parameters of a specific algorithm.
A comparison of different classification approaches for multitemporal SAR images data sets is provided in this work. The aim is to assess the performance of estimators of the backscatter temporal variability in terms of classification accuracy for a typical four-class problem. Different approaches in forming an appropriate feature vector are discussed and compared with multichannel classifiers like the fuzzy k-means. Finally, a classifier that employs a feature fusion step based on principal components analysis is proven promising since it provides increased classification accuracy and reduced computational complexity.
The Kullback-Leibler (KL) divergence, which is a fundamental concept in information theory used to quantify probability density differences, is employed in assessing the color content of digital images. For this purpose, digital images are encoded in the CIELAB color space and modeled as discrete random fields, which are assumed to be described sufficiently by 3-D probability density functions. Subsequently, using the KL divergence, a global quality assessment of an image is presented as the information content of the CIELAB encoding of the image relative to channel capacity. This is expressed by an image with "maximum realizable color information" (MRCI), which we define. Additionally, 1-D estimates of the marginal distributions in luminance, chroma, and hue are explored, and the proposed quality assessment is examined relative to KL divergences based on these distributions. The proposed measure is tested using various color images, pseudocolor representations and different renderings of the same scene. Test images and a MATLAB implementation of the measure are available online at http://www.ellab.physics.upatras.gr/PersonalPages/VTsagaris/research.htm.
An objective measure for evaluating the performance of pixel level fusion methods is introduced in this work. The proposed measure employs mutual information and conditional mutual information in order to assess and represent the amount of information transferred from the source images to the final fused greyscale image. Accordingly, the common information contained in the source images is considered only once in the formation of the final image. The measure can be used regardless the number of source images or the assumptions about the intensity values and there is no need for an ideal or test image. The experimental results clarify the usefulness of the proposed measure.
A novel procedure which aims in increasing the spatial resolution of multispectral data and simultaneously creates a high quality RGB fused representation is proposed in this paper. For this purpose, neural networks are employed and a successive training procedure is applied in order to incorporate in the network structure knowledge about recovering lost frequencies and thus giving fine resolution output color images. MERIS multispectral data are employed to demonstrate the performance of the proposed method.
In this work a pixel-level fusion technique for enhancing the visual interpretation of multispectral images is proposed. The technique takes into consideration the inherent high correlation of the RGB bands of natural color images, a fact strictly related to the color perception attributes of the human eye. The method provides dimensionality reduction in the multispectral vector space, while the resulting RGB color image tends to be perceptually optimal. The proposed method is compared with two other existing techniques.
In this paper a new approach for clutter and target characterization is proposed. The method is based on the use of Markov chains for representing the samples of both the clutter and the target. The mathematical representation of the clutter and the target is based on the transition matrix of an irreducible Markov chain. This kind of representation incorporates a full description of the underlying pdf as well as any order of statistical correlation. Among the useful and meaningful parameters of the transition matrix are its eigenvalues. In natural signals, transition matrices have only a small number of their elements with significant value. This fact can be used to device relatively simple Markov chain models for clutter representation. The target statistics can also be modeled by means of a Markov chain model. However, in this case, the model may be simpler since the target samples or pixels are highly correlated and their values are restricted to a smaller range compared to those of the clutter.
In this paper, we present a new statistical model to describe infrared images. The proposed pdf is a compound model derived from the Gaussian and the GT-pdf. Closed form expressions for the statistical moments of the model are derived. For specific values of its parameters the model ends up to be simpler pdfs. The proposed compound pdf models the thermal radiation from the background, the sunlight scattering as well as scintillating effects. We propose a parameter estimation technique which is based on the equivalence of experimental and theoretical moments. Experimental results are provided for model validation in real data as well as for demonstration of the segmentation procedure.